System and method for implementing a trust discretionary distribution tool

ABSTRACT

An embodiment of the present invention is directed to automated trust discretionary distribution decisions. The innovative system comprises a computer server configured to perform the steps of: receiving, via an electronic input, a trust beneficiary cash distribution request relating to a trust instrument; responsive to the trust beneficiary request, obtaining trust details relating to the trust instrument; applying, via a computer server, a trust decision predictor to the distribution request to generate a trust decision wherein the trust decision predictor considers a set of decision factors comprising the trust beneficiary cash distribution request, beneficiary details, trust details and applicability of governing restrictions; presenting, via an electronic interface, the trust decision; automatically executing the trust decision; and applying feedback data to refine and standardize the trust decision predictor.

CROSS REFERENCE TO RELATED APPLICATIONS

The application claims priority to U.S. Provisional Application62/650,332, filed Mar. 30, 2018, the contents of which are incorporatedherein in its entirety.

FIELD OF THE INVENTION

The invention relates generally to a system and method for implementinga trust discretionary distribution tool that automates decision makingand analysis.

BACKGROUND OF THE INVENTION

Trust beneficiary cash distribution requests are formally documented andapproved or denied by a trust officer. The decision making is at thetrust officer's discretion and oftentimes, personal bias and experienceswill affect the ultimate decisions. There is a current need to createconsistency in the decision making so that the approval and rejectioncriteria based on trust agreements are clear. For example, a trustbeneficiary may contact a trust officer and request an amount of moneyto purchase a new car. In response, the trust officer may analyze thetrust document to decide whether the request is an eligible request.This may require the trust officer to determine whether the request isreasonable under the terms of the trust and in line with the grantor'sgoals and the beneficiary's best interest. The determination may alsoconsider the trust situation, background of the beneficiary, currentcircumstances, etc. The current process is highly subjective and proneto inconsistencies.

These and other drawbacks exist.

SUMMARY OF THE INVENTION

According to one embodiment, the invention relates to a system thatimplements an automated trust discretionary distribution decisions. Thesystem comprises: a memory component that stores trust historical data;and a computer server coupled to the memory, the computer servercomprising a programmed computer processor configured to perform thesteps of: receiving, via an electronic input, a trust beneficiary cashdistribution request relating to a trust instrument; responsive to thetrust beneficiary request, obtaining trust details relating to the trustinstrument; applying, via a computer server, an automated data validatorthat validates whether the request has complete and valid input datapoints, a trust decision predictor to the distribution request togenerate a trust decision wherein the trust decision predictor considersa set of decision factors comprising the trust beneficiary cashdistribution request, beneficiary details, trust details andapplicability of governing restrictions presenting, via an electronicinterface, the trust decision; automatically executing the trustdecision; and applying feedback data to refine and standardize the trustdecision predictor. The trust decision predictor may also list thecontributory decision factors or decision rationale. For modelinterpretability, factors that contributed to each predicted decisionmay be identified.

The system may include a specially programmed computer system comprisingone or more computer processors, interactive interfaces, electronicstorage devices, and networks.

According to one embodiment, the invention relates to a method thatimplements an automated trust discretionary distribution decisions. Themethod comprises the steps of: receiving, via an electronic input, atrust beneficiary cash distribution request relating to a trustinstrument; responsive to the trust beneficiary request, obtaining trustdetails relating to the trust instrument; applying, via a computerserver, a trust decision predictor to the distribution request togenerate a trust decision wherein the trust decision predictor considersa set of decision factors comprising the trust beneficiary cashdistribution request, beneficiary details, trust details andapplicability of governing restrictions; presenting, via an electronicinterface, the trust decision; automatically executing the trustdecision; and applying feedback data to refine and standardize the trustdecision predictor.

The computer implemented system, method and medium described hereinprovide unique advantages to entities, organizations and other users,according to various embodiments of the invention. The innovative systemachieves significant efficiencies by shortening the time to make trustbeneficiary distribution decisions from weeks/months to minutes. Thesystem further realizes consistency and uniformity in renderingdistribution decisions. The innovative system may integrate with broaderTrust Distribution transactional and reporting systems. An embodiment ofthe present invention may be implemented for wealth management trustbusinesses and also packaged into a product which may be used with othertrusts and estates entities worldwide. These and other advantages willbe described more fully in the following detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to facilitate a fuller understanding of the present invention,reference is now made to the attached drawings. The drawings should notbe construed as limiting the present invention, but are intended only toillustrate different aspects and embodiments of the invention.

FIG. 1 is an exemplary flow diagram, according to an embodiment of thepresent invention.

FIG. 2 is an exemplary diagram of a data preprocessing and featuregeneration, according to an embodiment of the present invention.

FIG. 3 is an exemplary diagram of machine learning in DAR process,according to an embodiment of the present invention.

FIG. 4 is an exemplary illustration of a DAR Predictor, according to anembodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT(S)

The following description is intended to convey an understanding of thepresent invention by providing specific embodiments and details. It isunderstood, however, that the present invention is not limited to thesespecific embodiments and details, which are exemplary only. It isfurther understood that one possessing ordinary skill in the art, inlight of known systems and methods, would appreciate the use of theinvention for its intended purposes and benefits in any number ofalternative embodiments, depending upon specific design and other needs.

Trust instruments usually give guidance regarding the types ofdiscretionary distributions a trustee can make to beneficiaries.Discretionary powers may be defined in the trust instrument butgenerally these powers are subject to interpretation. Discretionarypowers are often used when making distributions to the beneficiaryregarding health, education, maintenance and support. To make suchdistribution decisions, a trustee may consider the size of the trust,distribution request, patterns of distribution, beneficiary needs aswell as long-term sustainability of the trust.

An embodiment of the present invention is directed to an automatedsystem that intelligently generates trust distribution decisions basedon various influencing factors from distribution requests. Theinnovative system utilizes a supervised learning method coupled withNatural Language Processing (NLP) to learning from historical decisionsmade by Trust officers over time. An embodiment of the present inventionconsiders the free-form textual context of the Trust distributionrequests and highly discretion based distribution decisions and alsoselected provision from Trust Governing Instruments that may influencediscretionary decision making. An embodiment of the present inventionleverages key factors as features for a model which may includedistribution request background, beneficiary background, correlationsamongst request, trust provision, trust officer's decision rationale andadditional factors such as trust value, amount requested, distributiontype (e.g., principal, income, etc.), request type (e.g., blanket,one-time, etc.).

An embodiment of the present invention may be directed to a tool thatformally requests and documents approval or denial of an action thatrequires a trustee, e.g., Corporate Trustee, to exercise discretion. Forexample, an embodiment of the present invention may capture backgroundinformation of the Cash Distribution request by beneficiary. In thisexample, free form comments may be considered vital given the uniquenature of requests by beneficiaries. The discretionary distributionprocess may consider the number of beneficiaries, history ofdiscretionary action requests (DAR), cash distribution decisions, trustsituation, other sources of income, tax, expenses, etc.

An embodiment of the present invention may also recognize patterns andfurther compare and/or refine patterns based on similar requests, eventsand/or circumstances. The system may analyze metrics and various sourcesof data. For example, the system may consider social media data toverify and legitimize requests. In this example, social media activitymay be used to confirm and/or contradict certain requests. The systemmay use public data to verify events, a requester's need and/or otherindependently verifiable data (e.g., travel plans, purchases, etc.).

The system may provide funds directly to a receiving entity (instead ofthe requester). For example, the system may receive a request topurchase a new car for $50,000. Once approved, the system may transferfunds for the purchase of a specific car to the car dealer (instead ofsending the funds to the requester). In addition, an embodiment of thepresent invention may apply Blockchain to the payments to ensuresecurity and that the funds are received by an intended recipient.

An embodiment of the present invention utilizes Artificial Intelligence(AI) Natural Language Capabilities to gain deeper insights intodiscretionary requests. For example, an embodiment of the presentinvention may intelligently extract reasons for a request category,e.g., living expenses, educational related expenses, etc. An embodimentof the present invention provides deeper insights compared totraditional tools which are not able to read through the texts.

An embodiment of the present invention may be directed to a system thatprovides an online portal or other web interface where distributionrequests may be made electronically. For example, a requester may accessan online portal and make requests using a drop down menu (or other userinterface) to identify a distribution need and where the distributionshould go. Depending on the complexity of the request, other forms ordata may be provided. For example, if a request involves $100 for amedical procedure, the response may be made immediately. If the requestinvolves $5 million dollars for seed capital, the system may requiresupporting documentation (e.g., business plan, presentations,statements, etc.) that may be communicated to the system (e.g.,uploaded, emailed, electronically transmitted via a link, etc.).Decisions may be presented via the same online portal or via other formsof communication, such as email, text, message, voicemail, etc. Thesystem may also provide the ability to view prior trust distributionsand further analyze and develop trends and patterns.

An embodiment of the present invention may be applied to the executionof the funds, e.g., back-end funding decisions. For example, when arequest is made, the system may determine how to best access the fundsfor distribution. The system may decide which accounts to retrieve thefunds from—whether the funds should be accessed from a principle accountor income account. An embodiment of the present invention may determinehow to best create liquidity from the investments. This may alsoconsider tax efficiencies and other considerations for the portfolio.The system may further consider whether an item (e.g., art, furniture,jewelry, property, etc.) should be sold to create additional cash andwhich decision is the most tax efficient or beneficial. In addition, anembodiment of the present invention may consider timing issues andbenefits. The system may also access historical data and also data thatindicates an optimal decision based on aligned goals and purposes. Otherconsiderations may be analyzed to develop an optimal funding strategy.

An embodiment of the present invention may provide guidance to a trustofficer or other representative or agent. According to another example,the system may be a fully automated system. According to yet anotherexample, the system may be a hybrid system where certain requests (e.g.,complex, high dollar amount, etc.) are forwarded to a trust officer andothers are handled automatically. For example, the system may identify aspecific distribution request that requires expertise in a particulararea and then forward the request to a trust officer with the requisiteknowledge and experience.

An embodiment of the present invention may also adapt to changing laws,regulations and/or other considerations that have an impact on trustdecisions. For example, a case may have a global impact on trustdecisions having a specific fact pattern. An embodiment of the presentinvention may identify other trust situations that may be impacted bythe case. This may also apply to changes in rules, state law,regulations, governing documents, etc. For example, a rule change mayalter how expenses may be allocated in a trust.

FIG. 1 is an exemplary flow diagram, according to an embodiment of thepresent invention. At step 110, data may be acquired. At step 112, datamay be processed. The data may include unstructured data and structureddata. At step 114, features may be analyzed. At step 116, a model may beselected and trained. At step 118, the model may be evaluated. The orderillustrated in FIG. 1 is merely exemplary. While the process of FIG. 1illustrates certain steps performed in a particular order, it should beunderstood that the embodiments of the present invention may bepracticed by adding one or more steps to the processes, omitting stepswithin the processes and/or altering the order in which one or moresteps are performed.

An embodiment of the present invention is directed to automating TrustDiscretionary Cash Distribution decision making processes. This may beconsidered a classic binary Classification Problem where classificationlabels may include “Request APPROVAL” or “Request DENIAL.” An embodimentof the present invention may leverage historical trust decision data fortraining, testing and/or evaluating supervised machine learning models.An embodiment of the present invention may use structured data points asfeatures. Furthermore, an embodiment of the present invention may useNatural Language Processing (NLP) techniques to extract features fromfree form text from the discretionary action request (DAR) data toaugment a feature list. Other techniques may be implemented inaccordance with the various embodiments of the present invention.

At step 110, data may be acquired. The data may be acquired from varioussources and may include structured and/or unstructured data from DARapplication databases and other sources. Structured data may includesize of the trust and amount requested by the beneficiary. Unstructureddata may include free form comments relating to information about thebeneficiary, trust agreement, cash distribution request and/or otherrelated data. An embodiment of the present invention may attach labels(or tags) to the gathered data. The data gathered may also contain afinal disposition of the discretionary request. The decision may be anAPPROVAL or a DENIAL. An embodiment of the present invention may usethis as labelled data for supervised Machine Learning.

An embodiment of the present invention may identify key factors thathave an impact on discretionary request. For example, the key factorsmay be extracted from DAR Comments/Text and DAR data Values, e.g., thekey words in comments across Request Background, Beneficiary Background,Decision, Correlation between Request to Provision, Trust Value, AmountRequested, Source Distribution Type (e.g., principal, income),Distribution Type (e.g., blanket, one-time), etc. The system may thenconvert the attributes into a format that a Machine Learning algorithmmay take in as an input.

At step 112, the data may be processed. As discussed above, the data mayinclude unstructured data and structured data. DAR requests may havefields that have free form comments as values. For example, documentfields may include Purpose, Background and Provision. Purpose maydescribe the purpose of a cash distribution request. Background mayidentify the background of the beneficiary. Provision may indicate therelevant provision from the governing Trust Document that is deemedimportant or relevant.

An embodiment of the present invention may implement NLP techniquesincluding keyword extraction, vocabulary building for sentiment analysisand/or document similarity.

For keyword extraction, an embodiment of the present invention mayconstruct a graph of words occurring in a defined window. Graph mayrepresent text and interconnect words or other text entities withmeaningful relations. In addition, keyword extraction may consider howimportant a word is to a document (or other collection of text). Thismay be represented by term or word frequency. According to anotherexample, inverse document frequency to be applied to find adiscriminating power of a word. An embodiment of the present inventionmay then rank the words, represented as graph vertices, and returnhighest scoring vertices as the extracted keywords from a document. Thisgraphical representation of words may consider word importance based oncontext and therefore semantically more meaningful.

For vocabulary building for sentiment analysis, after extractingkeywords, an embodiment of the present invention may create a vocabularyof POSITIVE Keywords and NEGATIVE keywords from a corpus of a TrustTraining Data Set. For example, positive keywords may represent keywordsthat occur in APPROVED requests and negative keywords may representkeywords that occur in DENIED requests. An embodiment of the presentinvention may also create a custom stop-word list to disregard commonwords in the Trust Domain.

For document similarity, an embodiment of the present invention mayrecognize that the more similar a cash distribution request was to itstrust provision, the higher the chances of its approval because thetrust document governs trust related transactions, including cashdistributions. According to an exemplary application, an embodiment ofthe present invention may use cosine similarity to get the documentsimilarity score of the request purpose to the trust document provision.Cosine similarity represents a metric used to measure how similardocuments are irrespective of their size. Other measurements ofsimilarity may be implemented.

An embodiment of the present invention may consider structured datapoints such as Size of the Trust and Amount Requested. The system mayalso use categorical data points such as Type of Distribution (e.g.,One-Type, Blanket, etc.) and Source of Distribution (e.g., Principal,Income or Both).

At step 114, Data Alerts may be defined and applied. Data Alerts may bebased on a predetermined condition, e.g., data is missing, request hasspecial characteristics that merit more review, special needs trust,explicit restrictions, relates to a depleting trust, etc.). For example,data alerts may be based on whether a high request ratio is identified.This may involve determining if a requested amount exceeds a percentageof a trust value. Another data alert may include whether a requester isnot the actual recipient. The system may determine whether the requestedamount is anomalous, which may be based on data analysis work andwhether the requested amount is several standard deviations away fromthe requested amount for the same category. Other data alerts may bebased on whether the trust account has an existing open blanket for asimilar purpose; whether specific restrictions apply in the governinginstrument and whether the beneficiary has other sources of income.

At step 116, features may be analyzed. An embodiment of the presentinvention may identify candidate features and run a correlation analysisto see if they are correlated to a DAR outcome. For example, the systemmay start off with more than 20 possible features and settled on 11features after the correlation analysis. Exemplary features may includethe following:

Actual Requested Amount—actual requested amount by beneficiary;

Alternative Asset Market Value—Non liquid asset values;

Negative Sentiment Score from Beneficiary Background;

Negative Sentiment Score from Purpose of the Request;

Net Income—for income generating trusts;

Purpose of Request Categories;

Total Trust Market Value;

Region—North American regions where the Trust is situated;

Sources of Cash Distribution—One time, recurring, blanket distributions;

Sources of Distribution—Income, Principal and Income & principal;

Total Number of Beneficiaries.

Other features may be defined and applied according to various otherapplications and environments.

Each DAR Request may be converted into a vector of values where thevector represents the chosen features. The illustration below is asample vector of values from unstructured and structured data.

Deny- Approve- Deny- Approve- Hop-Score Source- Score- Score- Score-Score- (purpose- Dist. 

Trust-Value 

Amount 

DistType 

Category 

Bg 

Bg 

Purpose 

Purpose 

to provision) 

2 

214104.50 

36671.24 

4 

3 

3 

2 

1 

1 

0.068932 

1 

45886.45 

6519.48 

4 

3 

6 

6 

6 

5 

0.482199 

At step 118, a model may be selected and trained. Once key factors areidentified, an embodiment of the present invention may feed them intoclassification algorithms, e.g., Logistic Regression, K-NearestNeighbors, Naïve Bayes, SVM (Support Vector Machine) and Decision Tree.

At step 120, a predictor may be applied with decision rationale. Thetrust decision predictor may identify contributory decision factors. Formodel interpretability, factors that contributed to each predicteddecision may be identified. For example, rationale for confirming adenial or an approval may include rationale related to the request,distribution, trust situation, background beneficiary and governinginstrument. Rationale relating to the request may include: request isnot appropriate; issue with use of funds; request has weak or incompletesupporting documentation or evidence; request is consistent withgoverning documents; emergency situation; risk mitigated with consent orrelease; request is for primary or appropriate beneficiary; etc.Rationale relating to the distribution may include: existing mandatorydistribution covering the request or need; existing discretionarydistribution covering the request or need; existing blanket or recurringdistribution covering the request or need; etc. Rationale relating tothe trust situation may include: amount is not appropriate; deletingtrust; impact of interests of multiple beneficiaries; trust isfinancially able to support distribution; size of trust; etc. Rationalerelating to the beneficiary background may include: other sources ofincome or assets; etc. Rationale relating to the governing instrumentmay include: inconsistency or consistency with governing documentterms/standards for distribution; etc.

At step 122, a feedback from Model Evaluation to Model Training may beapplied.

According to an exemplary embodiment, the model may be evaluated andtested against trust officer decisions. An embodiment of the presentinvention may provide an additional layer of verification for a corpusof Trust Discretionary Distribution Requests. From a processperspective, an embodiment of the present invention may include anadditional layer of verification when a Trust Distribution Request maybe added to training data.

An embodiment of the present invention may be directed to improvingmetrics. For example, the system may implement boosting techniques, suchas boosted trees, to increase accuracy. An embodiment of the presentinvention may also utilize Deep Learning Models.

An embodiment of the present invention may also analyze other machinelearning (ML) metrics. For example, the system may use standard modelaccuracy and confusion matrix. An embodiment of the present inventionmay analyze other ML metrics such as Area Under ROC Curve (AUC) oftenused as a measure of quality of the ML classification models as well aslogarithmic loss. An embodiment of the present invention may be directedto using the AUC to find various threshold settings of ML metrics inorder to find the optimal models.

An embodiment of the present invention may be directed to enhancing thealgorithm to clean up and/or add new features. Features may be conflatedinto one metric to avoid hints of correlation among features.

An embodiment of the present invention may be directed to improving akeyword extraction algorithm. An embodiment of the present invention mayapply intelligent mining techniques to search for stopwords, aside fromusing standard term frequencies in the vocabulary. Aside from filteringon POS tagging for keyword extraction, the system may experiment withpositional scores (e.g., score candidate keywords higher if they appearin a certain way in the document).

An embodiment of the present invention may be directed to improving asampling methodology as well. For example, the system may recognize anunbalanced class problem with “Approval” cases outnumbering “Denial”cases. The system may use a straightforward approach of evenly samplingamong the two classes, thus under-sampling the Approved cases. Anembodiment of the present invention may use other techniques to addressimbalanced classes. These techniques may include, but are not limitedto, k-means cluster centroids sampling and one-side selection sampling.

FIG. 2 is an exemplary diagram of a data preprocessing and featuregeneration, according to an embodiment of the present invention. Asshown in FIG. 2 , governing instruments 208 and original trust decisions210 may be analyzed by a language extractor 212. Extracted language,e.g., keywords may be stored in a Trust Instrument Information datastore 214. Trust Instrument Information 214, Beneficiary Information216, Trust Account Information 218 and Discretionary Request Information220 may be used to generate Alerts 222 and for Model Training 224, whichmay then feed to generate Predictions 226. According to an exemplaryillustration, the data from one or more of: Trust Instrument Information214, Beneficiary Information 216, Trust Account Information 218 andDiscretionary Request Information 220 may be converted to vectors, e.g.,Featurized Vectors, that may be used to perform model training at 224.The order illustrated in FIG. 2 is merely exemplary. While the processof FIG. 2 illustrates certain steps performed in a particular order, itshould be understood that the embodiments of the present invention maybe practiced by adding one or more steps to the processes, omittingsteps within the processes and/or altering the order in which one ormore steps are performed.

FIG. 3 is an exemplary operationalization flow, according to anembodiment of the present invention. FIG. 3 illustrates a TrainingModule 310, a Predictor Module 320 and a Model Scoring Module 330.Training Module 310 may access training data set 312 for modelevaluation 314 to identify a best model at 316. Predictor Module 320 mayinclude a Predictor 326 and apply Machine Learning 324. Model ScoringModule 330 may evaluate Trust Officer Decisions from DAR Application 322and Machine Learning outputs at 332 and identify decision differences at334. Production Data Set 340 may receive the output and provide data toTraining Data set 312. A Re-Train process may be applied at 350. Theorder illustrated in FIG. 3 is merely exemplary. While the process ofFIG. 3 illustrates certain steps performed in a particular order, itshould be understood that the embodiments of the present invention maybe practiced by adding one or more steps to the processes, omittingsteps within the processes and/or altering the order in which one ormore steps are performed.

FIG. 4 is an exemplary illustration of a DAR Predictor, according to anembodiment of the present invention. An embodiment of the presentinvention may apply machine learning recommendation system that suggestsan approval or denial of a discretionary action request (DAR). MachineLearning is a subfield of Artificial Intelligence where a machine'slearning algorithm enables it to identify patterns in observed data,build models that explain the world, and predict things based onprevious data and patterns. An embodiment of the present invention mayapply Machine Learning to learn and build a model from historical DARs.For example, an embodiment of the present invention may learn fromprevious DAR decisions that the Trust Team has made by extracting thepatterns and/or factors that have historically affected DAR decisionmaking. The system may then make recommendations on new DARs based onwhat is has learned from prior DARs. For example, the system may usehistorical data (e.g., 10 years of DAR data) to build a Machine Learningmodel. The system may continuously learn from more DARs as these DARsare received.

According to an embodiment of the present invention, the system mayinclude components comprising Recommendation Engine 410, ConfidenceScore Generator 412, Decision Factors 414, DAR Flags 416 and Feedbackcomponent 418. Recommendation Engine 410 may generate a MachineRecommended Approval or Denial of a DAR. Confidence Score Generator 412may generate a score that represents confidence in approving or denyinga DAR. Decision Factors 414 may represent factors that may be consideredwhen making an approval or denial recommendation. DAR Flags 416 mayrepresent special characteristics that may require it to be subject forreview (e.g., special needs trust, high request amount, trust depletion,etc.). Feedback component 418 may represent a mechanism for an evaluatorto input rationale for agreeing or disagreeing with a recommendation.

An embodiment of the present invention may make recommendations on cashdistributions from Principal and Income Distributions.

According to an embodiment of the present invention, Decision Factorsmay include Request data, Beneficiary data, Trust characteristics, andGovernment Instrument.

Request data may include whether the request may be consideredacceptable or appropriate (e.g., Acceptability, Appropriateness).Request data may consider purpose of the request. This may include aRequest Category field in DAR or may be mined from Free Text. Requestdata may also consider Recipient of the Request. This may be an actualrecipient of the requested Cash Distribution, which may be mined fromFree Text or other source of data. Request data may also include anamount requested which may be represented as an amount requested, amountrequested over trust market value, over an average amount approved for apurpose category and/or other indication of an amount or value. Requestdata may also encompass Distribution Details which may include source ofcash distribution, type of cash distribution, etc.

Beneficiary data may represent a background validation. Beneficiary datamay include general background (e.g., acceptable/questionable backgroundof the beneficiary, as mined from Free Text or other source of data).Beneficiary data may also include a beneficiary's distribution historywhich may include most common requested purpose, total Amount alreadyreceived, prior discretionary distributions, etc. Other resourcesavailable for the Beneficiary may also be considered. For example,beneficiary information may be identified from social media and othersources.

Trust characteristics may include a possible impact from requesteddistribution. Trust Details may include a number of beneficiaries, trustmarket value, liquidity, trust size category (e.g., large/medium/small).

Governing Instrument may consider any specific restrictions fordistribution.

An embodiment of the present invention may make a recommendations withincomplete data however a corresponding confidence score may adjusted.In addition, a flag may be applied for additional review.

With an embodiment of the present invention, the data points that aTrust Officer has taken into consideration when making the decision maybe stored and maintained in the pertinent DAR fields. In addition,attachments may be mentioned and summarized in the pertinent DAR fields.In addition, restrictions may be in the Relevant DiscretionaryProvisions. Relevant and requested documentation is uploaded.

An embodiment of the present invention is directed to facilitatingstandardized data collection used in decision making and furtherstandardize factors that affect decision making. For example, anembodiment of the present invention may standardize data collection whenrecording a DAR thereby achieving a more robust record keeping practice.

An embodiment of the present invention may be integrated with otherapplications including a Trust and Estates business. For example, anembodiment of the present invention may provide a trust team with ahistorical view of how the Trust and Estates (T&E) business has decidedon a similar DAR in the past.

An embodiment of the present invention may be directed to improving duediligence process by ensuring that known factors are taken intoconsideration in making a decision. For DARs that meet certain criteriathat would qualify it as a low-risk DAR (e.g., dollar amount, etc.), anembodiment of the present invention may apply a check on these DARs andmake a recommendation to proceed with the Trust Officer decision orrecommend further analysis by the Team Lead. Other recommendations maybe made as well.

Other variations may be implemented. For example, an embodiment of thepresent invention may use external sources to retrieve beneficiaryinformation, such as sources of income, for validation. An embodiment ofthe present invention may provide a side-by-side comparison of a trustteam decision and rationale with decision and decision factors. This mayinclude prediction factors (e.g., type of cash distribution, amountapproved, percentage of requested amount to trust market value,background for approval, source of cash distribution, etc.) and anindication of denial and approval.

An embodiment of the present invention may provide standardized feedbackgathering tools for learning purposes. This may include informationconcerning the request, trust situation, beneficiary background,governing instrument, etc.

Aside from identifying the rationale of the decision, an embodiment ofthe present invention may also determine where the data was receivedfrom. This may assist in addressing data gaps. For example, anembodiment of the present invention may provide the ability to identifya source of information through an interactive icon. Sources ofinformation may include: information in DAR fields, information in DARattachments, outside information (e.g., personal knowledge, phone call,conversation, etc.) and other sources.

The foregoing examples show the various embodiments of the invention inone physical configuration; however, it is to be appreciated that thevarious components may be located at distant portions of a distributednetwork, such as a local area network, a wide area network, atelecommunications network, an intranet and/or the Internet. Thus, itshould be appreciated that the components of the various embodiments maybe combined into one or more devices, collocated on a particular node ofa distributed network, or distributed at various locations in a network,for example. As will be appreciated by those skilled in the art, thecomponents of the various embodiments may be arranged at any location orlocations within a distributed network without affecting the operationof the respective system.

As described above, the various embodiments of the present inventionsupport a number of communication devices and components, each of whichmay include at least one programmed processor and at least one memory orstorage device. The memory may store a set of instructions. Theinstructions may be either permanently or temporarily stored in thememory or memories of the processor. The set of instructions may includevarious instructions that perform a particular task or tasks, such asthose tasks described above. Such a set of instructions for performing aparticular task may be characterized as a program, software program,software application, app, or software.

It is appreciated that in order to practice the methods of theembodiments as described above, it is not necessary that the processorsand/or the memories be physically located in the same geographicalplace. That is, each of the processors and the memories used inexemplary embodiments of the invention may be located in geographicallydistinct locations and connected so as to communicate in any suitablemanner. Additionally, it is appreciated that each of the processorand/or the memory may be composed of different physical pieces ofequipment. Accordingly, it is not necessary that the processor be onesingle piece of equipment in one location and that the memory be anothersingle piece of equipment in another location. That is, it iscontemplated that the processor may be two or more pieces of equipmentin two or more different physical locations. The two distinct pieces ofequipment may be connected in any suitable manner. Additionally, thememory may include two or more portions of memory in two or morephysical locations.

As described above, a set of instructions is used in the processing ofvarious embodiments of the invention. The servers may include softwareor computer programs stored in the memory (e.g., non-transitory computerreadable medium containing program code instructions executed by theprocessor) for executing the methods described herein. The set ofinstructions may be in the form of a program or software or app. Thesoftware may be in the form of system software or application software,for example. The software might also be in the form of a collection ofseparate programs, a program module within a larger program, or aportion of a program module, for example. The software used might alsoinclude modular programming in the form of object oriented programming.The software tells the processor what to do with the data beingprocessed.

Further, it is appreciated that the instructions or set of instructionsused in the implementation and operation of the invention may be in asuitable form such that the processor may read the instructions. Forexample, the instructions that form a program may be in the form of asuitable programming language, which is converted to machine language orobject code to allow the processor or processors to read theinstructions. That is, written lines of programming code or source code,in a particular programming language, are converted to machine languageusing a compiler, assembler or interpreter. The machine language isbinary coded machine instructions that are specific to a particular typeof processor, i.e., to a particular type of computer, for example. Anysuitable programming language may be used in accordance with the variousembodiments of the invention. For example, the programming language usedmay include assembly language, Ada, APL, Basic, C, C++, COBOL, dBase,Forth, Fortran, Java, Modula-2, Pascal, Prolog, REXX, Visual Basic,JavaScript and/or Python. Further, it is not necessary that a singletype of instructions or single programming language be utilized inconjunction with the operation of the system and method of theinvention. Rather, any number of different programming languages may beutilized as is necessary or desirable.

Also, the instructions and/or data used in the practice of variousembodiments of the invention may utilize any compression or encryptiontechnique or algorithm, as may be desired. An encryption module might beused to encrypt data. Further, files or other data may be decryptedusing a suitable decryption module, for example.

In the system and method of exemplary embodiments of the invention, avariety of “user interfaces” may be utilized to allow a user tointerface with the mobile devices or other personal computing device. Asused herein, a user interface may include any hardware, software, orcombination of hardware and software used by the processor that allows auser to interact with the processor of the communication device. A userinterface may be in the form of a dialogue screen provided by an app,for example. A user interface may also include any of touch screen,keyboard, voice reader, voice recognizer, dialogue screen, menu box,list, checkbox, toggle switch, a pushbutton, a virtual environment(e.g., Virtual Machine (VM)/cloud), or any other device that allows auser to receive information regarding the operation of the processor asit processes a set of instructions and/or provide the processor withinformation. Accordingly, the user interface may be any system thatprovides communication between a user and a processor. The informationprovided by the user to the processor through the user interface may bein the form of a command, a selection of data, or some other input, forexample.

The software, hardware and services described herein may be providedutilizing one or more cloud service models, such asSoftware-as-a-Service (SaaS), Platform-as-a-Service (PaaS), andInfrastructure-as-a-Service (IaaS), and/or using one or more deploymentmodels such as public cloud, private cloud, hybrid cloud, and/orcommunity cloud models.

Although the embodiments of the present invention have been describedherein in the context of a particular implementation in a particularenvironment for a particular purpose, those skilled in the art willrecognize that its usefulness is not limited thereto and that theembodiments of the present invention can be beneficially implemented inother related environments for similar purposes.

What is claimed is:
 1. A system that implements automated trustdiscretionary distribution decisions, the system comprising: a memorycomponent that stores trust historical data; and a computer servercoupled to the memory, the computer server comprising a programmedcomputer processor configured to perform the steps of: receiving, via anelectronic input, a trust beneficiary cash distribution request relatingto a trust instrument, the trust beneficiary cash distribution requestincluding both structured data and unstructured data; responsive to thetrust beneficiary request, obtaining trust details relating to the trustinstrument; applying, via a computer server, a trust decision predictorpowered by a machine learning model to the distribution request toextract select features from the trust beneficiary cash distributionrequest using natural language processing (NLP) and to generate a trustdecision based on the extracted features, wherein the trust decisionpredictor considers a set of decision factors comprising the trustbeneficiary cash distribution request, beneficiary details, trustdetails and applicability of governing restrictions; generating, by themachine learning model, a graph of words occurring in a defined window;presenting, via an electronic interface, the trust decision;automatically executing the trust decision; determining a differencebetween a result rendered by a trust officer and a result rendered bythe machine learning model; and modifying the machine learning model toaddress the determined difference between the result rendered by thetrust officer and the result rendered by the machine learning model. 2.The system of claim 1, wherein the trust decision predictor applies themachine learning model that is built from historical DiscretionaryAction Requests by extracting patterns and factors that affect DARdecision making.
 3. The system of claim 1, wherein the trust decisionpredictor applies a confidence score that represents a confidence inapproving or denying a Discretionary Action Request.
 4. The system ofclaim 1, further comprising applying feedback data to refine the trustdecision predictor, wherein the feedback represents an input rationalefor agreeing or disagreeing with a recommendation.
 5. The system ofclaim 1, wherein the trust decision predictor generates a back-endfunding decision for the trust beneficiary cash distribution request. 6.The system of claim 1, wherein the trust decision predictor considerssocial media data to generate the trust decision.
 7. The system of claim1, wherein executing the trust decision comprises electronicallytransmitting funds to a third party recipient.
 8. The system of claim 1,wherein the trust decision predictor applies flags that indicateadditional review.
 9. The system of claim 1, wherein the trustbeneficiary cash distribution request comprises a purpose of therequest, recipient of the request, amount requested and distributiondetails.
 10. The system of claim 1, wherein the beneficiary detailscomprise distribution history.
 11. A method that implements automatedtrust discretionary distribution decisions, the method comprising thesteps of: receiving, via an electronic input, a trust beneficiary cashdistribution request relating to a trust instrument, the trustbeneficiary cash distribution request including both structured data andunstructured data; responsive to the trust beneficiary request,obtaining trust details relating to the trust instrument; applying, viaa computer server, a trust decision predictor powered by a machinelearning model to the distribution request to extract select featuresfrom the trust beneficiary cash distribution request using naturallanguage processing (NLP) and to generate a trust decision based on theextracted features, wherein the trust decision predictor considers a setof decision factors comprising the trust beneficiary cash distributionrequest, beneficiary details, trust details and applicability ofgoverning restrictions; generating, by the machine learning model, agraph of words occurring in a defined window; presenting, via anelectronic interface, the trust decision; automatically executing thetrust decision; determining a difference between a result rendered by atrust officer and a result rendered by the machine learning model; andmodifying the machine learning model to address the determineddifference between the result rendered by the trust officer and theresult rendered by the machine learning model.
 12. The method of claim11, wherein the trust decision predictor applies the machine learningmodel that is built from historical Discretionary Action Requests byextracting patterns and factors that affect DAR decision making.
 13. Themethod of claim 11, wherein the trust decision predictor applies aconfidence score that represents a confidence in approving or denying aDiscretionary Action Request.
 14. The method of claim 11, furthercomprising applying feedback data to refine the trust decisionpredictor, wherein the feedback represents an input rationale foragreeing or disagreeing with a recommendation.
 15. The method of claim11, wherein the trust decision predictor generates a back-end fundingdecision for the trust beneficiary cash distribution request.
 16. Themethod of claim 11, wherein the trust decision predictor considerssocial media data to generate the trust decision.
 17. The method ofclaim 11, wherein executing the trust decision comprises electronicallytransmitting funds to a third party recipient.
 18. The method of claim11, wherein the trust decision predictor applies flags that indicateadditional review.
 19. The method of claim 11, wherein the trustbeneficiary cash distribution request comprises a purpose of therequest, recipient of the request, amount requested and distributiondetails.
 20. The method of claim 11, wherein the beneficiary detailscomprise distribution history.